Search results for: teaching and learning English

Authors: Mehran Memari, Bita Asadi

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Second language (L2) classrooms in academic contexts usually consist of learners with diverse L2 proficiency levels. One solution for managing such heterogeneous classes and addressing individual needs of students is to improve learner autonomy by using technological innovations such as blogging. The focus of this study is on investigating the effects of blogging on improving the quality of Iranian university students' writings. For this aim, twenty-six Iranian university students participated in the study. Students in the experimental group (n=13) were required to blog daily while the students in the control group (n=13) were asked to write a daily schedule using paper and pencil. After a 3-month period of instruction, the five last writings of the students from both groups were rated by two experienced raters. Also, students' attitudes toward the traditional method and blogging were surveyed using a questionnaire and a semi-structured interview. The research results showed evidences in favor of the students who used blogging in their writing program. Also, although students in the experimental group found blogging more demanding than the traditional method, they showed an overall positive attitude toward the use of blogging as a way of improving their writing skills. The findings of the study have implications for the incorporation of computer-assisted learning in L2 academic contexts.

Keywords: blogging, computer-assisted learning, paper and pencil, writing

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Authors: Kafeero Frank

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This study aimed at developing a frame work for open source software adoption in an institution of higher learning in Uganda, with the case of KIU as a study area. There were mainly four research questions based on; individual staff interaction with open source software forum, perceived FOSS characteristics, organizational characteristics and external characteristics as factors that affect open source software adoption. The researcher used causal-correlation research design to study effects of these variables on open source software adoption. A quantitative approach was used in this study with self-administered questionnaire on a purposively and randomly sampled sample of university ICT staff. Resultant data was analyzed using means, correlation coefficients and multivariate multiple regression analysis as statistical tools. The study reveals that individual staff interaction with open source software forum and perceived FOSS characteristics were the primary factors that significantly affect FOSS adoption while organizational and external factors were secondary with no significant effect but significant correlation to open source software adoption. It was concluded that for effective open source software adoption to occur there must be more effort on primary factors with subsequent reinforcement of secondary factors to fulfill the primary factors and adoption of open source software. Lastly recommendations were made in line with conclusions for coming up with Kyambogo University frame work for open source software adoption in institutions of higher learning. Areas of further research recommended include; Stakeholders’ analysis of open source software adoption in Uganda; Challenges and way forward. Evaluation of Kyambogo University frame work for open source software adoption in institutions of higher learning. Framework development for cloud computing adoption in Ugandan universities. Framework for FOSS development in Uganda IT industry

Keywords: open source software., organisational characteristics, external characteristics, cloud computing adoption

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Authors: Bahloul Amel

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The main aim of this research was to investigate the perspectives of English language teachers and learners on the effect of test techniques on reading comprehension, test performance and assessment. The research has also aimed at finding the differences between teacher and learner perspectives, specifying the test techniques which have the highest effect, investigating the other factors affecting reading comprehension, and comparing the results with the similar studies. In order to achieve these objectives, perspectives and findings of different researchers were reviewed, two different questionnaires were prepared to collect data for the perspectives of teachers and learners, the questionnaires were applied to 26 learners and 8 teachers from the University of Batna (Algeria), and quantitative and qualitative data analysis of the results were done. The results and analysis of the results show that different test techniques affect reading comprehension, test performance and assessment at different percentages rates.

Keywords: reading comprehension, reading assessment, test performance, test techniques

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Boonsri Lertviriyachit

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The objective of this research is to investigate the treatment of psoriasis through Thai traditional medicine in the selected areas of 2 east coast provinces; Samudprakarn Province and Chantaburi Province. The informants in this study were two famous and accepted Thai traditional doctors, who have more than 20 year experiences. Data were collected by in depth interviews and participant-observation method. The research instrument included unstructured interviews, camera, and cassette tape to collect data analyzed by descriptive statistics. The results revealed that the 2 Thai traditional doctors were 54 and 85 years old with 25 and 45 years of treatment experiences. The knowledge of Thai traditional medicine was transferred from generations to generations in the family. The learning process was through close observation as an apprentice with the experience ones and assisted them in collecting herbs and learning by handling real case in individual situations. Before being doctors, they had to take exam to get the Thai traditional medical certificate. Knowledge of being Thai traditional doctors included diagnosis and find to the suitable way of treatment. They have to look into disorder physical fundamental factors such as blood circulation, lymph, emotion, and food consumption habit. It is important that the treatment needs to focus on balancing the fundamental factors and to observe contraindication.

Keywords: Thai traditional medicine, psoriasis, Samudprakarn Province, Chantaburi Province

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Authors: Mohd Jaish Siddiqui

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Laryngeal TB is extremely rare in the pediatric population, accounting for 1% of all cases. Here, we present a case of laryngeal TB with miliary tuberculosis and tuberculous encephalitis, presented with sore throat, hoarseness, severe cough and, acute obstruction the larynx, sputum for AFB was negative, T-SPOT was positive and X-pert was positive, bronchoscopy revealed multiple nodules and edema around the larynx, epiglottis, bilateral arytenopharyngeal folds and vocal cord. Enhanced MRI revealed multiple small nodules in bilateral cerebral hemispheres and right thalamus, however CSF was negative. We reviewed the LTB cases that were published up to 2021. A total of twenty fine cases were identified in English literature. The most common manifestation was hoarseness of voice with 80% followed by stridor 40% of cases. Pulmonary involvement was found in 36% of cases, whereas, 45% of cases had no underlying TB. We did not find any case who developed tuberculous encephalitis in the literature.

Keywords: laryngeal tb, treatment, tuberculous encephalitis, children

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Authors: Ya Ting Tang

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Social learning theory has told us that the cognitions of gender roles come from learning. Thus, the images of gender roles which media describes will shape our cognitions. Taiwan and Japan are both in the East Asian cultural Sphere, and more or less influenced by the traditional Chinese culture. But our social structure and changes must be different. Others, the study also concerns that, with the rise of female consciousness in society, whether the female stereotype in drama of Taiwan and Japan are improved. This research first uses content analysis to analyze drama of Taiwan and Japan in 2003 and 2013 on how to shape female roles. Then use text analysis to conduct a qualitative analysis. Result of this study showed that drama on how to depict women indeed have changed, women are no longer just talk about love, but can serve as president or doctor, and show its mettle in the workplace. In Japanese drama, the female roles have diverse occupation than Taiwanese drama, and not just a background character set. But in most Taiwanese drama, female roles are given a career, but it always put emphasis on women emotionally. To include, although the stereotype in the drama of Taiwan and Japan are improved, female will still be derided due to their ages, love or marriage situations. Taiwanese drama must depict the occupation of female more diverse and let the female roles have more space to play, rather than focusing on romance which women of any occupation can have.

Keywords: female images, stereotype, TV drama, gender roles

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Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

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This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

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Authors: Saori Fujimoto, Hiromi Kawasaki, Mari Murakami, Yoko Ueno

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Background: Coronavirus disease (COVID-19) has been spreading throughout the world. In Japan, many nursing universities have conducted online clinical practices to secure students’ learning opportunities. In the field of women’s health nursing, even after the pandemic ended, it will be worthwhile to utilize online practice in declining birthrate and reducing the burden of mothers. This study examined the learning effects of conducting online visits for mothers with infants during the COVID-19 pandemic by nursing students to enhance the students’ ability to carry out the online practice even in ordinary times effectively. Methods: Students were divided into groups of three, and information on the mothers was assessed, and the visits were planned. After role-play was conducted by the students and teachers, an online visit was conducted. The analysis target was the self-evaluation score of nine students who conducted online visits in June 2020 and had consented to participate. The evaluation contents included three items for assessment, two items for planning, one item for ethical consideration, five items for nursing practice, and two items for evaluation. The self-evaluation score ranged from 4 (‘Can do with a little advice’) to 1 (‘Can’t do with a little advice’). A univariate statistical analysis was performed. This study was approved by the Ethical Committee for Epidemiology of Hiroshima University. Results: The items with the highest mean (standard deviation) scores were ‘advocates for the dignity and the rights of mothers’ (3.89 (0.31)) and ‘communication behavior needed to create a trusting relationship’ (3.89 (0.31)).’ Next were the ‘individual nursing practice tailored to mothers (3.78 (0.42))’ and ‘review own practice and work on own task (3.78 (0.42)).’ The mean (standard deviation) of the items by type were as follows: three assessment items, 3.26 (0.70), two planning items, 3.11 (0.49), one ethical consideration item, 3.89 (0.31), five nursing practice items, 3.56 (0.54), and two evaluation items, 3.67 (0.47). Conclusion: The highest self-evaluations were for ‘advocates for the dignity and the rights of mothers’ and ‘communication behavior needed to create a trusting relationship.’ These findings suggest that the students were able to form good relationships with the mothers by improving their ability to effectively communicate and by presenting a positive attitude, even when conducting health visits online. However, the self-evaluation scores for assessment and planning were lower than those of ethical consideration, nursing practice, and evaluation. This was most likely due to a lack of opportunities and time to gather information and the need to modify and add plans in a short amount of time during one online visit. It is necessary to further consider the methods used in conducting online visits from the following viewpoints: methods of gathering information and the ability to make changes through multiple visits.

Keywords: infants, learning effects, mothers, online visit practice

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Authors: Leila Kargar, Homa Abri, Golsa Safai

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The occurrence of chest pain among geriatrics could be considered as a predictor of heart issues. There is a need for attention to this pain among this population. This review paper has tried to collect the recent data with attention to the chest pain among geriatrics. This review paper has focused on specific keywords, including chest pain, heart issues, and geriatrics, among published papers from 2015 till 2020. To collect data for this purpose, Scopus, Web of Sciences, and PubMed were used. After inserting related papers to the Endnote, an independent researcher checked the abstract, and papers with unclear methods or non-English language were excluded. Finally, 7-papers were included in this review paper. The findings of those papers showed that chest pain could be a predictor for heart issues, and also, there is a direct relationship between chest pain and heart issues among geriatrics. So, early detection and an accurate decision could be helpful to prevent heart issues in this population.

Keywords: pain, heart issue, geriatrics, health

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Authors: A. Metioui

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Research related to the student’s conceptual difficulties in sciences, particularly in the field of physics, are relatively numerous. In this work, we will analyze the results of qualitative research conducted with 80 elementary preservice teachers from Quebec in Canada on their understandings after studying the free fall bodies. First, we will illustrate the paper-pencil questionnaire built for this purpose. Then we will give the analysis of the experimental data. The results show that, even though there is a continuing physics education, many misconceptions persist despite the teaching provided.

Keywords: pre-service teachers, elementary school, conceptual difficulties, free fall bodies

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Authors: Andrea Scapolo, Zaya Rustamova, Arturo Matute Castro

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The “Voices in Dissent” initiative aims at collecting and making available in a digital format, testimonies, letters, and other narratives produced by victims of political oppression from different geographical spaces across the Atlantic. By recovering silenced voices behind the official narratives, this open-access online database will provide indispensable tools for rewriting the history of authoritarian regimes from the margins as memory debates continue to provoke controversy among academic and popular transnational circles. In providing an extensive database of non-hegemonic discourses in a variety of political and social contexts, the project will complement the existing European and Latin-American studies, and invite further interdisciplinary and trans-national research. This digital resource will be available to academic communities and the general audience and will be organized geographically and chronologically. “Voices in Dissent” will offer a first comprehensive study of these personal accounts of persecution and repression against determined historical backgrounds and their impact on collective memory formation in contemporary societies. The digitalization of these texts will allow to run metadata analyses and adopt comparatist approaches for a broad range of research endeavors. Most of the testimonies included in our archive are testimonies of trauma: the trauma of exile, imprisonment, torture, humiliation, censorship. The research on trauma has now reached critical mass and offers a broad spectrum of critical perspectives. By putting together testimonies from different geographical and historical contexts, our project will provide readers and scholars with an extraordinary opportunity to investigate how culture shapes individual and collective memories and provides or denies resources to make sense and cope with the trauma. For scholars dealing with the epistemological and rhetorical analysis of testimonies, an online open-access archive will prove particularly beneficial to test theories on truth status and the formation of belief as well as to study the articulation of discourse. An important aspect of this project is also its pedagogical applications since it will contribute to the creation of Open Educational Resources (OER) to support students and educators worldwide. Through collaborations with our Library System, the archive will form part of the Digital Commons database. The texts collected in this online archive will be made available in the original languages as well as in English translation. They will be accompanied by a critical apparatus that will contextualize them historically by providing relevant background information and bibliographical references. All these materials can serve as a springboard for a broad variety of educational projects and classroom activities. They can also be used to design specific content courses or modules. In conclusion, the desirable outcomes of the “Voices in Dissent” project are: 1. the collections and digitalization of political dissent testimonies; 2. the building of a network of scholars, educators, and learners involved in the design, development, and sustainability of the digital archive; 3. the integration of the content of the archive in both research and teaching endeavors, such as publication of scholarly articles, design of new upper-level courses, and integration of the materials in existing courses.

Keywords: digital archive, dissent, open educational resources, testimonies, transatlantic studies

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Authors: Muhammad Zubair Khan, Yugyung Lee

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Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with bi-directional LSTM embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization for reducing internal covariate shift in data distributions. The empirical evidence shows a promising response to our method compared with other semantic segmentation techniques.

Keywords: deep learning, semantic segmentation, image analysis, pixels connection, convolution neural network

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Authors: Vangmayee. V. Subban, Somashekara H. S, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: Phonological processing is critical in learning to read alphabetical and non-alphabetical languages. However, its role in learning to read Kannada an alphasyllabary is equivocal. The literature has focused on the developmental role of phonological awareness on reading. To the best of authors knowledge, the role of phonological memory and phonological naming has not been addressed in alphasyllabary Kannada language. Therefore, there is a need to evaluate the comprehensive role of the phonological processing skills in Kannada on word decoding skills during the early years of schooling. Aim and Objectives: The present study aimed to explore the phonological processing abilities and their role in learning to decode pseudowords in children learning to read the Kannada language during initial years of formal schooling between 5.6 to 8.6 years. Method: In this cross sectional study, 60 typically developing Kannada speaking children, 20 each from Grade I, Grade II, and Grade III between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. Phonological processing abilities were assessed using an assessment tool specifically developed to address the objectives of the present research. The assessment tool was content validated by subject experts and had good inter and intra-subject reliability. Phonological awareness was assessed at syllable level using syllable segmentation, blending, and syllable stripping at initial, medial and final position. Phonological memory was assessed using pseudoword repetition task and phonological naming was assessed using rapid automatized naming of objects. Both phonological awareneness and phonological memory measures were scored for the accuracy of the response, whereas Rapid Automatized Naming (RAN) was scored for total naming speed. Results: The mean scores comparison using one-way ANOVA revealed a significant difference (p ≤ 0.05) between the groups on all the measures of phonological awareness, pseudoword repetition, rapid automatized naming, and pseudoword reading. Subsequent post-hoc grade wise comparison using Bonferroni test revealed significant differences (p ≤ 0.05) between each of the grades for all the tasks except (p ≥ 0.05) for syllable blending, syllable stripping, and pseudoword repetition between Grade II and Grade III. The Pearson correlations revealed a highly significant positive correlation (p=0.000) between all the variables except phonological naming which had significant negative correlations. However, the correlation co-efficient was higher for phonological awareness measures compared to others. Hence, phonological awareness was chosen a first independent variable to enter in the hierarchical regression equation followed by rapid automatized naming and finally, pseudoword repetition. The regression analysis revealed syllable awareness as a single most significant predictor of pseudoword reading by explaining the unique variance of 74% and there was no significant change in R² when RAN and pseudoword repetition were added subsequently to the regression equation. Conclusion: Present study concluded that syllable awareness matures completely by Grade II, whereas the phonological memory and phonological naming continue to develop beyond Grade III. Amongst phonological processing skills, phonological awareness, especially syllable awareness is crucial for word decoding than phonological memory and naming during initial years of schooling.

Keywords: phonological awareness, phonological memory, phonological naming, phonological processing, pseudo-word decoding

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

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Authors: P. P. L. Kwan, D. K. S. Chan

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Organizations are constantly changing in today’s business environment. Research findings have revealed that overcoming resistance and getting employees ready for change is a crucial driver for organizational change success. Thus, change adaptability has become a more prominent selection criterion used in many organizations. Although change readiness could be situation-specific, employees’ personality, emotion, and cognition should also be crucial factors in shaping their readiness. However, relatively little research has focused on the roles of individual characteristics in organizational changes. The present study examines the relations between individual characteristics and change readiness with the aim to validate a model, which proposes three types of individual attributes as antecedents to change readiness. The three attributes considered are trait cynicism, positive affectivity, and personal valence covering personality, emotional, and cognitive aspects respectively. The model also hypothesizes that relations between the three antecedents and change readiness will be moderated by a positive mental resource known as psychological capital (PsyCap), which consists of hope, optimism, efficacy and resilience; and a learning culture within the organization. We are currently collecting data from a targeted sample size of 300 Hong Kong employees. Specifically, participants complete a questionnaire which was designed to measure their perceived change efficacy in response to three scenarios commonly happened in the workplace (i.e., business acquisition, team restructuring, and information system change) as a measure of change readiness, as well as the aforementioned individual characteristics. Preliminary analysis provides some support to the hypotheses. That is, employees who are less cynical in personality and more positive in their cognition and affectivity particularly welcome the potential changes in their organizations. Further data collection and analyses are continuously carried out for a more definitive conclusion. Our findings will shed light on employee selection; and on how strengthening positive psychological resources and promoting the culture of learning organization among employees may enhance the chance to succeed for organizations undergoing change.

Keywords: learning organization, psychological capital, readiness for change, employee selection

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Authors: Prasanna Haddela

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Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.

Keywords: evolved search queries, Sinhala document classification, Lucene Sinhala analyzer, interpretable text classification, genetic algorithm

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Authors: Chia Yu Chen, Shu Fen Wu, Chen-Fuh Lam, I-Ling Tsai, Shu Jiuan Chen, Yen Ling Liu

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Aim: It remains undetermined that whether increased work stress may affect the job satisfaction and career loyalty among nursing staffs in the operating room. The long-term goal of this study is to lengthen the professional life of operating room nurses by attenuating the work stress and enhancing their contentment in work. Method: This was a cross-sectional, descriptive study performed in a metropolitan teaching hospital in the southern Taiwan between May 2017 to July 2017. A structured self-administered questionnaire, modified from the Occupational Stress Indicator-2 (OSI-2) and Maslach Burnout Inventory (MBI) manual was collected from the operating room nurses. Chi-square test was used to analyze the categorical data and Pearson correlation was used to analyze the association between two numerical datasets (SPSS version 20.0). Results: The response rate was 80% (80/100) and a total of 73 (73%) completed forms were eventually proceeded for analysis. The average scores for work stress and job satisfaction of the operating room nurses were 145.96±32.91 and 47.38±6.07, respectively. The correlation coefficients of work stress versus job satisfaction and organizational identity were (r=-0.338, p=0.003 and r=-0.354, p=0.002), respectively. There were more nurses who took rotating shift quitted works from the operating room than those who took only dayshift (2＝5.176, p<0.05). Nurses who reported of having lower job satisfaction were associated with significantly higher turnover intention (t=3.714, p< 0.01). Following multivariate regression analysis, rotating shift and low job satisfaction were identified as the two independent predictors of intention to quit from working in the operating room. Conclusion: Our study clearly demonstrates that increased work stress significantly attenuates job satisfaction and organizational identity. Rotating shift is associated with higher work stress, lower job satisfaction, and higher turnover intention, which is consistent with the previous surveys carried out in the department of medical technology. Therefore, improvement of working quality in the operating rooms is essential to increase the retain intention of the well-trained nursing staffs. Further investigation into types of work shifts and other strategies of attenuating stress in workplace is currently undertaken in order to improve the job satisfaction and to decrease turnover intention in the operating room.

Keywords: rotating shift, work stress, job satisfaction, turnover intention

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Authors: Jin Lam, Joe Au

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This study aims to investigate the impact of practicing expressive textile arts on the underprivileged people namely, ex-offenders after taking a three-month textile arts and fashion creativity workshops from a service-learning subject, offered by the Hong Kong Polytechnic University in May 2016. In this service-learning subject, the subject lecturers, students and ex-offenders co-designed various expressive textile artworks together. During the creative process, the ex-offenders could enhance their self-confidence and rebuild a satisfactory identity through practicing expressive textile arts and fashion creativity. Ten textile arts prototypes in the format of fashion garments were presented in a mini fashion show and an exhibition, both at the Hong Kong Polytechnic University in July 2016. A quantitative research method was adopted and a questionnaire survey was conducted in this study. The research findings suggest that positive impacts are found on the ex-offenders’ perceptions of ‘feelings and thoughts before attending the workshops’, ‘feelings and thoughts during the workshops’, ‘attitude toward the textile arts materials’, and ‘attitude toward the expressive textile artworks’.

Keywords: creativity, design, expressive textile arts, fashion, underprivileged people

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Authors: Laura de Zwaan, Tracey West

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There is a sustained observable gender gap in financial literacy, with females consistently having lower levels than males. This research explores the knowledge and experiences of high school students in Australia aged 14 to 18 in order to understand how this gap can be improved. Using a predominantly qualitative approach, we find evidence to support impacts on financial literacy from financial socialization and socio-economic environment. We also find evidence that current teaching and assessment approaches to financial literacy may disadvantage female students. We conclude by offering recommendations to improve the way financial literacy education is delivered within the curriculum.

Keywords: financial literacy, financial socialization, gender, maths

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee

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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.

Keywords: vaccination, NFHS, machine learning, public health

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Authors: Davut Disci

Abstract:

Objective: to measure the factors affecting expectations and intentions of using mobile phone in educational contexts by university students, using advanced equations and modeling techniques. Design and Methodology: According to the literature, Mobile Addiction, Parental Surveillance- Safety/Security, Social Relations, and Mobile Behavior are most used terms of defining mobile use of people. Therefore these variables are tried to be measured to find and estimate their effects on expectations and intentions of using mobile phone in educational context. 421 university students participated in this study and there are 229 Female and 192 Male students. For the purpose of examining the mobile behavior and educational expectations and intentions, a questionnaire is prepared and applied to the participants who had to answer all the questions online. Furthermore, responses to close-ended questions are analyzed by using The Statistical Package for Social Sciences(SPSS) software, reliabilities are measured by Cronbach’s Alpha analysis and hypothesis are examined via using Multiple Regression and Linear Regression analysis and the model is tested with Structural Equation Modeling(SEM) technique which is important for testing the model scientifically. Besides these responses, open-ended questions are taken into consideration. Results: When analyzing data gathered from close-ended questions, it is found that Mobile Addiction, Parental Surveillance, Social Relations and Frequency of Using Mobile Phone Applications are affecting the mobile behavior of the participants in different levels, helping them to use mobile phone in educational context. Moreover, as for open-ended questions, participants stated that they use many mobile applications in their learning environment in terms of contacting with friends, watching educational videos, finding course material via internet. They also agree in that mobile phone brings greater flexibility to their lives. According to the SEM results the model is not evaluated and it can be said that it may be improved to show in SEM besides in multiple regression. Conclusion: This study shows that the specified model can be used by educationalist, school authorities to improve their learning environment.

Keywords: education, mobile behavior, mobile learning, technology, Turkey

Procedia PDF Downloads 410

Authors: Maria Ezziani, Rabie Zine, Amine Amar, Ilhame Kissani

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This paper introduces a predictive model for urban transportation engineering, which is vital for efficient traffic management. Utilizing comprehensive datasets and advanced statistical techniques, the model accurately forecasts travel times by considering temporal variations and traffic dynamics. Machine learning algorithms, including regression trees and neural networks, are employed to capture sequential dependencies. Results indicate significant improvements in predictive accuracy, particularly during peak hours and holidays, with the incorporation of traffic flow and speed variables. Future enhancements may integrate weather conditions and traffic incidents. The model's applications range from adaptive traffic management systems to route optimization algorithms, facilitating congestion reduction and enhancing journey reliability. Overall, this research extends beyond travel time estimation, offering insights into broader transportation planning and policy-making realms, empowering stakeholders to optimize infrastructure utilization and improve network efficiency.

Keywords: predictive analytics, traffic flow, travel time estimation, urban transportation, machine learning, traffic management

Procedia PDF Downloads 63

Authors: Sanja Bracun, Nikolina Kasunic

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After graduation, the student population of applied sciences will become the population of employees on IT experts’ positions or "just" business users of certain IT technologies for which the level of awareness of existing cybersecurity risks is extremely important. This research results define the current cybersecurity awareness level of students at Zagreb University of Applied Sciences (TVZ), what can be useful not only for teaching staff to form a curriculum related to cybersecurity more accurately but also to employers to know what to expect from their future employees regarding cybersecurity awareness level.

Keywords: student population cybersecurity awareness, cybersecurity awareness, cybersecurity, applied sciences students

Procedia PDF Downloads 237

Authors: Lulu Sun

Abstract:

In Addition is an after-school mathematics program in which students and their parents build mathematical confidence and competence by solving problems they curious about. It is a program consists of mix-grade from 4th to 6th grade of 10 to 20 students, including math problem solving and other activities. This partnership will focus on the relationship between the In-Addition and the parents’ engagement; in this kind of partnership, it has the In-Addition program teaching and the family engagement. This partnership is purpose to building cooperation between the program and parents, strengthening the links between the program and families.

Keywords: program-family, family engagement, positive bias, partnership

Procedia PDF Downloads 180

Authors: Zhao Luqian, Wang Yafei

Abstract:

Participation in music activities is beneficial for enhancing wellbeing, especially for aged people (Creech, 2013). Looking at percussion group in particular, it can facilitate a sense of belonging, relaxation, energy, and productivity, learning, enhanced mood, humanising, seems of accomplishment, escape from trauma, and emotional expression (Newman, 2015). In health literatures, group drumming is effective in reducing stress and improving multiple domains of social-motional behaviors (Ho et al., 2011; Maschi et al., 2010) because it offers a creative and mutual learning space that allows patients to establish a positive peer interaction (Mungas et al., 2014; Perkins, 2016). However, very few studies have investigated the effect of group drumming from the aspect of patients’ needs. Therefore, this study focuses on the discussion of patients' specific needs within mental health and explores how group percussion may meet their needs. Seligman’s (2011) five core elements of mental health were applied as patients’ needs in this study: (1) Positive emotions; (2) Engagement; (3) Relationships; (4) Meaning and (5) Accomplishment. 12 participants aged 57- 80 years were interviewed individually. The researcher also had observation in four drumming groups simultaneously. The results reveal that group drumming could improve participants’ mental wellbeing. First, it created a therapeutic health care environment extending beyond the elimination of boredom, and patients could focus on positive emotions during the session of group drumming. Secondly, it was effective in satisfying patients’ level of engagement. Thirdly, this study found that joining a percussion group would require patients to work on skills such as turn-taking and sharing. This equal relationship is helpful for releasing patients’ negative mood and thus forming tighter relationships between and among them. Fourthly, group drumming was found to meet patients’ meaning needs through offering them a place of belonging and a place for sharing. Its leaner-oriented approach engaged patients by a sense of belonging, accepting, connecting, and ownership. Finally, group drumming could meet patients’ needs for accomplishment through the learning process. The inclusive learning process, which indicates there is no right or wrong throughout the process, allowed patients to make their own decisions. In conclusion, it is difficult for patients to achieve positive emotions, engagement, relationships, meanings, and accomplishments in a hospital setting. Drumming can be practiced for enhancement in terms of reducing patients’ negative emotions and improving their experiences in a hospital through enriched social interaction and sense of accomplishment. Also, it can help patients to enhance social skills in a controlled environment.

Keywords: group drumming, hospital, mental health, music psychology

Procedia PDF Downloads 79

Authors: Ismail Baniadam, Nafisa Tadayyon, Javid Fereidoni

Abstract:

This study aimed to investigate medical students’ attitudes toward some teachers’ talking features regarding their gender in the Iranian context. To do so, 60 male and 60 female medical students of Urmia University of Medical Sciences (UMSU) participated in the research. A researcher made Likert-type questionnaire which was initially piloted and was used to gather the data. Comparing the four different factors regarding the features of teacher talk, it was revealed that visual and extra-linguistic information factor, Lexical and syntactic familiarity, Speed of speech, and the use of Persian language had the highest to the lowest mean score, respectively. It was also indicated that female students rather than male students were significantly more in favor of speed of speech and lexical and syntactic familiarity.

Keywords: attitude, gender, medical student, teacher talk

Procedia PDF Downloads 164

Authors: Lianzhong Zhang, Chao Huang

Abstract:

Sea-land segmentation is a basic step in many tasks such as sea surface monitoring and ship detection. The existing sea-land segmentation algorithms have poor segmentation accuracy, and the parameter adjustments are cumbersome and difficult to meet actual needs. Also, the current sea-land segmentation adopts traditional deep learning models that use Convolutional Neural Networks (CNN). At present, the transformer architecture has achieved great success in the field of natural images, but its application in the field of radar images is less studied. Therefore, this paper proposes a sea-land segmentation method based on the transformer architecture to strengthen edge supervision. It uses a self-attention mechanism with a gating strategy to better learn relative position bias. Meanwhile, an additional edge supervision branch is introduced. The decoder stage allows the feature information of the two branches to interact, thereby improving the edge precision of the sea-land segmentation. Based on the Gaofen-3 satellite image dataset, the experimental results show that the method proposed in this paper can effectively improve the accuracy of sea-land segmentation, especially the accuracy of sea-land edges. The mean IoU (Intersection over Union), edge precision, overall precision, and F1 scores respectively reach 96.36%, 84.54%, 99.74%, and 98.05%, which are superior to those of the mainstream segmentation models and have high practical application values.

Keywords: SAR, sea-land segmentation, deep learning, transformer

Procedia PDF Downloads 155